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检索条件"任意字段=2023 International Conference on Cognitive Computing and Complex Data, ICCD 2023"

共 718 条记录，以下是491-500 订阅

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9th international Symposium on Applied computing for Software and Smart systems, ACSS 2022

9th International Symposium on Applied Computing for Softwar...

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9th international Symposium on Applied computing for Software and Smart systems, ACSS 2022

ISBN: (纸本)9789811967900

The proceedings contain 18 papers. The special focus in this conference is on Applied computing for Software and Smart systems. The topics include: BEN-CNN-BiLSTM: A Model of Consequential Document Set Identification of Bengali Text;A Machine Learning Model for Automatic Sleep Staging Based on Single-Channel EEG Signals;Deep Learning-Based Prediction of Time-Series Single-Cell RNA-Seq data;stress Analysis Using Machine Learning;Deep Learning Towards Brain Tumor Detection Using MRI Images;An Efficient Targeted Influence Maximization Based on Modified IC Model;a Novel Unmanned Near Surface Aerial Vehicle Design Inspired by Owls for Noise-Free Flight;data Quality Driven Design Patterns for Internet of Things;path Dependencies in Bilateral Relationship-Based Access Control;a Robust Approach to Document Skew Detection;MBLEACH: Modified Blockchain-Based LEACH Protocol;community Detection in Large and complex Networks Using Semi-Local Similarity Measure;Evaluation of SVM Kernels with Multiple Uncorrelated Feature Subsets Selected by Multiple Correlation Methods for Reflection Amplification DDoS Attacks Detection;BLRS: An Automated Land Records Management System Using Blockchain Technology;classification of Kathakali Asamyuktha Hasta Mudras Using Naive Bayes Classifier and Convolutional Neural Networks;Multi-objective Fuzzy Reliability Redundancy Allocation for xj -out-of- mj System Using Fuzzy Rank-Based Multi-objective PSO.

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To study about various existing schemes in this field of cloud enabled cognitive radio environment

To study about various existing schemes in this field of clo...

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New Frontiers in Communication, Automation, Management and Security (ICCAMS), international conference on

作者： S Manjunatha T N Manjunath K G Mohan B Rekha Computer Science and Engineering SJCIT Chickballapur Visvesvaraya Technological University Belagavi Information Science and Engineering BMSIT&M Yelahanka Computer Science Engineering GITAM University Bangalore Computer Science & Engineering SJBIT Bangalore

ISBN: (数字)9798350317060

ISBN: (纸本)9798350317077

The cognitive Radio-(CR) promises consistent communications performance thanks to its adaptive learning and reconfiguration capabilities. C4ISR systems require enormous amounts of data, which can be stored and processed in the cloud. Today’s wireless technologies, on the other side, have trouble quickly and reliably moving different types of data on battlefields. For cloud access, data communication with reliable and time is the assurance from CR networks. Connecting CRs inclusively with the cloud gets around each one’s performance problems. Cloud enabled cognitive radio is made effective with future internet for application provisioning.

关键词： Cloud computing Military computing Prototypes Transforms cognitive radio Reliability Security

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Optimising the Cuckoo Search Algorithm for Improved Quality of Service in cognitive Radio ad hoc Networks

Optimising the Cuckoo Search Algorithm for Improved Quality ...

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international conference on Advances in Big data, computing and data Communication Systems (icABCD)

作者： Ramahlapane Lerato Moila Mthulisi Velempini Department of Computer Science University of Limpopo Polokwane South Africa

This study proposes an optimised routing scheme, called OCS-AODV, for cognitive Radio Ad Hoc Networks (CRAHNs) to enhance Quality of Service (QoS). The scheme applies the Cuckoo Search (CS) algorithm optimised with a fitness function to improve the performance of the Ad Hoc On-Demand Distance Vector (AODV). The objective of the study is to evaluate the proposed scheme's performance with respect to delay, packet loss, packet delivery ratio and throughput. The literature review shows that the existing routing protocols have limitations which impact performance in dynamic environments. The proposed OCS-AODV scheme aims to address these limitations by selecting reliable paths based on a fitness function that considers the lifetime of nodes, reliability, and available buffer capacity. The simulation results have shown that the OCS-AODV scheme outperforms the CS-DSDV and ACO-AODV schemes in terms of PDR, packet loss, delay, and throughput. The study concludes that the proposed scheme improves the QoS of routing in CRAHNs. However, the use of a single fitness function may not be optimal for all network scenarios. Multiple fitness functions may be considered in future and the schemes be evaluated in real-world CRAHNs

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international conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

International Conference on Innovative Technologies in Intel...

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international conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2022

ISBN: (纸本)9783031290770

The proceedings contain 86 papers. The special focus in this conference is on international conference on Innovative Technologies in Intelligent Systems and Industrial Applications. The topics include: Analysing the Impact of Support Plans on Telehealth Services Users with complex Needs;trend and Behaviour Changes in Young People Using the New Zealand Mental Health Services;securing Cloud Storage data Using Audit-Based Blockchain Technology—A Review;data Security in Hybrid Cloud computing Using AES Encryption for Health Sector Organization;cyber Warfare: Challenges Posed in a Digitally Connected World: A Review;surveilling Systems Used to Detect Lingering Threats on Dark Web;early Attack Detection and Resolution in Sensor Nodes to Improve IoT Security;exploring Cyber Security Challenges of Multi-cloud Environments in the Public Sector;Histogram of Oriented Gradients (HOG) and Haar Cascade with Convolutional Neural Network (CNN) Performance Comparison in the Application of Edge Home Security System;data Security Risk Mitigation in the Cloud Through Virtual Machine Monitoring;analysis of Knocking Potential Based on Vibration on a Gasoline Engine for Pertalite and Pertamax Turbo Using Signal Processing Methods;AI-Based Video Analysis for Driver Fatigue Detection: A Literature Review on Underlying datasets, Labelling, and Alertness Level Classification;a Study of Information and Communications Technology Students e-Platform Choices as Technopreneur;occupational Safety and Health Training in Virtual Reality Considering Human Factors;block Chain Technology and Internet of Thing Model on Land Transportation to Reduce Traffic Jam in Big Cities;a Marketing Strategy for Architects Using a Virtual Tour Portfolio to Enhance Client Understanding.

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EEG-based Emotion Recognition in Immersive Virtual Reality: Meeting the Requirement of Accuracy and Computational Efficiency 23

EEG-based Emotion Recognition in Immersive Virtual Reality: ...

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7th international conference on Digital Signal Processing, ICDSP 2023

作者： Pei, Guanxiong Fan, Cunhang Li, Taihao Jin, Jia Wang, Rui Research Institute of Artificial Intelligence Zhejiang Lab China School of Computer Science and Technology Anhui University China Laboratory of Applied Brain and Cognitive Sciences Shanghai International Studies University China School of Business and Management Shanghai International Studies University China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China

ISBN: (纸本)9781450398626

Virtual reality technology provides a strong sense of immersion and interactivity. It is widely used in the fields of anxiety relief, fear therapy, and depression regulation. However, objectively evaluating the emotional intervention effect of virtual reality technology is a difficult problem. The main purpose of this paper is to explore the use of EEG signals to identify individual emotional states in virtual reality scenarios and to improve the computational efficiency and recognition accuracy of emotional valence. To induce the target emotional state of the participants, we established a relatively standard emotion-induced virtual reality video library. The EEG data of the participants were collected synchronously as they watched the virtual reality video. The results show that the emotion recognition performance of multiple features (energy spectrum, differential entropy, differential asymmetry, and rational asymmetry) is better than that of a single feature. The radial basis function neural network (RBFNN) performed better than the deep belief network (DBN). RBFNN achieves the highest average classification accuracy of 91.1%. By combining the feature selection (F-test) method with the RBFNN, an ideal classification performance can be maintained with computational efficiency improvements. Furthermore, it is demonstrated that the features extracted from the theta band outperform features extracted from other bands in emotional valence decoding. These results may contribute to the application of EEG-based affective computing technology in the field of psychological rehabilitation and assessment. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Virtual reality

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Edge-Cloud Collaboration Architecture for Efficient Web-Based cognitive Services

Edge-Cloud Collaboration Architecture for Efficient Web-Base...

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international conference on Big data and Smart computing (BIGCOMP)

作者： Zhaoyan Wang In-Young Ko School of Computing Korea Advanced Institute of Science and Technology Daejeon Republic of Korea

Many web services are often latency-sensitive, have high network capabilities, and have computing resource limitations, as demonstrated by deep neural network (DNN) model-based web applications. In this work, a DNN model-based web application providing visual cognitive services is implemented and explored to address latency-sensitive problems, taking into account the broad practicability and importance of web object recognition tasks in intelligent applications and modern systems. We propose a collaborative architecture of enduser, edge server, and cloud server, employing a binary offloading strategy to reduce the upload rate of images while ensuring good detection performance, thereby reducing the response time. Detection scenario-oriented data augmentation is carried out on the dataset to improve detection accuracy. Finally, we compare the performance of the proposed approach with traditional object recognition services running entirely on the cloud. The experimental results show that the detection scenariooriented strategy significantly improves the detection accuracy of fine-trained YOLOv5 models on the PASCAL VOC 2012 dataset. Compared with the traditional cloud-based architecture, the proposed edge-cloud collaboration architecture can detect more objects and reduce response time efficiently.

关键词： Visualization Web services Image edge detection Computational modeling Collaboration Computer architecture Time factors

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Modelling of Anti-amyloid-Beta Therapy for Alzheimer's Disease 10th

Modelling of Anti-amyloid-Beta Therapy for Alzheimer's Disea...

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10th international Work-conference on Bioinformatics and Biomedical Engineering (IWBBIO)

作者： Pal, Swadesh Melnik, Roderick Wilfrid Laurier Univ MS2Discovery Interdisciplinary Res Inst M3AI Lab Waterloo ON N2L 3C5 Canada BCAM Basque Ctr Appl Math Bilbao 48009 Spain

ISBN: (纸本)9783031349522;9783031349539

A healthy brain clears different types of debris with the help of specialized glial cells. These cells contiguously tile the entire central nervous system (CNS), exert many essential complex functions in the healthy CNS, and maintain a healthy balance in the brain. However, over age, these cells fail to control the healthy balance of the proteins and cause different neurodegenerative diseases, one of which is Alzheimer's disease (AD). In AD, insoluble amyloid-beta plaques accumulate in the extracellular space along with neurofibrillary tangles (NFTs) inside the brain cells. In this paper, we have developed a model and studied the accumulation of amyloid-beta plaques and NFTs along with an anti-amyloid-beta therapy applied in the treatment of the disease. Based on these studies, we have demonstrated the dynamics of the modelling therapy such that the drug helps clear a subsequent amount of amyloid-beta plaques in each dose. Numerical simulations have been used to show different long-term outcomes of the model. To further analyze the disease progression in the brain and its treatment, we have integrated brain connectome data in the network model as part of our developed modelling framework.

关键词： Alzheimer's disease amyloid-beta plaques phosphorylated tau drug-controlled treatments neurodegeneration cognitive declines data-driven models brain connectome

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Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability 37

Bayesian nonparametric (non-)renewal processes for analyzing...

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37th conference on Neural Information Processing Systems, NeurIPS 2023

作者： Liu, David Lengyel, Máté Department of Engineering University of Cambridge United Kingdom Department of Cognitive Science Central European University United Kingdom

ISBN: (纸本)9781713899921

Neural spiking activity is generally variable, non-stationary, and exhibits complex dependencies on covariates, such as sensory input or behavior. These dependencies have been proposed to be signatures of specific computations, and so characterizing them with quantitative rigor is critical for understanding neural computations. Approaches based on point processes provide a principled statistical framework for modeling neural spiking activity. However, currently, they only allow the instantaneous mean, but not the instantaneous variability, of responses to depend on covariates. To resolve this limitation, we propose a scalable Bayesian approach generalizing modulated renewal processes using sparse variational Gaussian processes. We leverage pathwise conditioning for computing nonparametric priors over conditional interspike interval distributions and rely on automatic relevance determination to detect lagging interspike interval dependencies beyond renewal order. After systematically validating our method on synthetic data, we apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice and hippocampal place cells in rats running along a linear track. Our model exhibits competitive or better predictive power compared to state-of-the-art baselines, and outperforms them in terms of capturing interspike interval statistics. These results confirm the importance of modeling covariate-dependent spiking variability, and further analyses of our fitted models reveal rich patterns of variability modulation beyond the temporal resolution of flexible count-based approaches. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Mammals

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Advancements in AI-Assisted Plant Disease Diagnosis

Advancements in AI-Assisted Plant Disease Diagnosis

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international conference on computing, Communication, and Intelligent Systems (ICCCIS)

作者： Anmol Chhabra Tarun Saroch Ruchika Gupta Aditya Shukla Raghav Computer Science & Engineering Department Centre of Excellence - Data Science and Cognitive Computing Chandigarh University Mohali India

Plant disease detection is a crucial task in agriculture to ensure healthy crop production. It is vital to identify plant diseases early in order to avert economic and environmental damages. A Machine learning-based approach including Deep Learning techniques for identifying plant disease was developed using Grape leaf images in this work. Inception v3, Squeezenet, VGG-16, and VGG-19 are some embedders that are used in the suggested method to convert raw image data into a fixed-length vector representation, also known as a “embedding” or “feature vector.” Our research concludes that CNN stands out with exceptional accuracy above 99.70% compared to classifiers like Naive Bayes, Logistic Regression, and SVM. Additionally, Inception V3 proved to be an efficient feature extractor, enhancing feature representation and reducing processing time.

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Policy-based Role and Access Modeling

Policy-based Role and Access Modeling

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IEEE international Workshop on Intelligent data Acquisition and Advanced computing Systems: Technology and Applications

作者： Noha Ayman Mariam Kiryakos Caroline Sabty Alia El Bolock Slim Abdennadher Informatics and Computer Science German International University Cairo Egypt Swansea University Swansea Wales United Kingdom

In this paper, we present an approach based on Attribute Based Access Control (ABAC) and Risk Adaptive Access Control (RAdAC) to improve the reliability of the risk assessment and authorization process. The model has been designed to adapt to the complex and ever-changing nature of modern campus networks by incorporating location information. This added layer of risk assessment enhances the ABAC model by adding a level of authorization to it.

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